The present invention relates generally to the field of wind power generation, and more particularly to predicting wind power generation on a computer.
Wind power is the use of air flow through wind turbines to mechanically power generators for electricity. Wind power is a renewable energy resource, but it is intermittent and uncontrollable. Although the power produced by wind tends to be consistent from year to year, it exhibits significant variation over shorter time scales. Wind is therefore often used in conjunction with other electric power sources to give a reliable supply. Due to the erratic nature of wind power, electric utilities may find it difficult to adequately integrate wind power into the electric grid, resulting in involuntary curtailment.
Wind power curtailment is a reduction in the output of a wind turbine or wind farm into the electric grid from what it could otherwise produce, given available wind, and is usually involuntary. Curtailment is typically induced by grid operators or utilities because of transmission congestion or lack of transmission access, or due to oversupply of power during low load periods, for example at night when substantial wind power may be generated but demand is low. As involuntary curtailment may lead to a loss of revenue, curtailment management is an important tool in maintaining system energy balance.
Wind farms handle curtailment in various ways. One way is to simply take some wind turbines offline or orient them away from the wind direction. Another is to use some of the produced energy to create reverse torque that reduces the speed of a turbine blade. Wind farms may also redirect some of the excess generated electricity to storage batteries. Typically, a wind farm may receive a signal from a grid operator or utility to curtail power to a specific level and may cause the wind turbines to reduce their output accordingly.
In a smart grid, grid operators strive to ensure that power plants produce the right amount of electricity at the right time, in order to consistently and reliably meet demand. Because the grid has limited storage capacity, maintaining a balance between electricity supply and demand prevents blackouts and other cascading problems. Grid operators typically send a signal to power plants every few seconds to control the balance between the total amount of power injected into the grid and the total power withdrawn. Sudden power generation shortfalls or excesses due to intermittency may require a grid operator to maintain more reserve power in order to quickly act to keep the grid balanced.
As mentioned, one approach to dealing with wind power intermittency is the use of storage technology, such as large-scale batteries, to store excess power or augment supply in case of a shortfall. However, batteries are expensive and susceptible to wear when subjected to excessive cycling. More accurate and flexible power output models may be advantageous in reducing such cycling.
A method of accurately predicting the output of wind power plants for various future time periods would be a valuable tool, allowing wind farm owners to operate more economically, for example by reducing involuntary curtailment due to oversupply, and allowing grid operators and utilities to reduce the costs of integrating sources of wind power generation into the existing grid, for example, by scaling down conventional resources when sufficient wind generation is predicted.